08_word2vec.ipynb

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      "metadata": {
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        "colab_type": "text"
      },
      "source": [
        "# Word2Vec\n",
        "\n",
        "Inspired by https://www.tensorflow.org/tutorials/text/word_embeddings."
      ]
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          "height": 34
        },
        "outputId": "3af0cddb-9a1d-41a7-a08f-506588df4a7b"
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 1,
      "outputs": [
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          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
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          "height": 34
        },
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      },
      "source": [
        "%pylab inline"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
        "id": "2Fj37gU8VIRf",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "import tensorflow as tf\n",
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      ],
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      "outputs": []
    },
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      "source": [
        "(train_data, test_data), info = tfds.load(\n",
        "    'imdb_reviews/subwords8k', \n",
        "    split = (tfds.Split.TRAIN, tfds.Split.TEST), \n",
        "    with_info=True, as_supervised=True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews (80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/subwords8k/0.1.0...\u001b[0m\n"
          ],
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            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
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            "Instructions for updating:\n",
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          "text": [
            "\r\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/subwords8k/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPb95p1cV7Dh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder = info.features['text'].encoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukuUlGntV93N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "padded_shapes = ([None],())\n",
        "train_batches = train_data.shuffle(1000).padded_batch(10, padded_shapes = padded_shapes)\n",
        "test_batches = test_data.shuffle(1000).padded_batch(10, padded_shapes = padded_shapes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnDvMatxVSdx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "f8de557d-a822-41dd-8c67-d544e59c05e2"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Embedding(encoder.vocab_size, 16),\n",
        "  tf.keras.layers.GlobalAveragePooling1D(),\n",
        "  tf.keras.layers.Dense(16, activation='relu'),\n",
        "  tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 16)          130960    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 131,249\n",
            "Trainable params: 131,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2C7CkdGyVt6M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7VR2HEnW1GE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "9fb399ae-57a5-41f5-8624-f3c09792af88"
      },
      "source": [
        "history = model.fit(\n",
        "    train_batches,\n",
        "    epochs=10,\n",
        "    validation_data=test_batches, validation_steps=20)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2500/2500 [==============================] - 26s 10ms/step - loss: 0.5039 - accuracy: 0.7567 - val_loss: 0.3901 - val_accuracy: 0.8250\n",
            "Epoch 2/10\n",
            "2500/2500 [==============================] - 17s 7ms/step - loss: 0.2836 - accuracy: 0.8941 - val_loss: 0.3133 - val_accuracy: 0.8700\n",
            "Epoch 3/10\n",
            "2500/2500 [==============================] - 17s 7ms/step - loss: 0.2264 - accuracy: 0.9191 - val_loss: 0.2979 - val_accuracy: 0.8750\n",
            "Epoch 4/10\n",
            "2500/2500 [==============================] - 17s 7ms/step - loss: 0.1959 - accuracy: 0.9307 - val_loss: 0.2343 - val_accuracy: 0.8900\n",
            "Epoch 5/10\n",
            "2500/2500 [==============================] - 18s 7ms/step - loss: 0.1727 - accuracy: 0.9396 - val_loss: 0.2326 - val_accuracy: 0.9050\n",
            "Epoch 6/10\n",
            "2500/2500 [==============================] - 18s 7ms/step - loss: 0.1528 - accuracy: 0.9468 - val_loss: 0.3580 - val_accuracy: 0.8550\n",
            "Epoch 7/10\n",
            "2500/2500 [==============================] - 18s 7ms/step - loss: 0.1404 - accuracy: 0.9528 - val_loss: 0.2443 - val_accuracy: 0.9000\n",
            "Epoch 8/10\n",
            "2500/2500 [==============================] - 18s 7ms/step - loss: 0.1306 - accuracy: 0.9552 - val_loss: 0.3297 - val_accuracy: 0.8650\n",
            "Epoch 9/10\n",
            "2500/2500 [==============================] - 17s 7ms/step - loss: 0.1152 - accuracy: 0.9632 - val_loss: 0.3406 - val_accuracy: 0.8650\n",
            "Epoch 10/10\n",
            "2500/2500 [==============================] - 17s 7ms/step - loss: 0.1068 - accuracy: 0.9645 - val_loss: 0.4240 - val_accuracy: 0.8850\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8SgQrbJW1RS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}